System for selection of regulated products

ABSTRACT

A system for categorizing, visualizing, and recommending cannabis products based on objective data of the chemical composition of products is described. This system allows cannabis products to be visualized and compared based on their chemical composition, even by laypeople with little or no knowledge of the underlying objective data. A recommendation system is built upon this which can recommend products for consumption, including a user interface for receiving user input, which includes at least one of demographic data, desired level of psychoactivity, or prior experience with the product, a merchant interface for receiving merchant input, including at least one of general data descriptive of the regulated product, lab data descriptive of the regulated products chemical composition, and user data descriptive of subjective effects of the regulated product on a user; and a decision engine making a recommendation of product according to correlation of the lab data with the user data.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The invention disclosed herein relates to a system of categorization ofa regulated product, and in particular, to an online system forrecommending product exploration paths to users navigating through thisproduct categorization schema and identifying locations for acquisitionof such products.

2. Description of the Related Art

Retail purchasing by consumers has conventionally been done at alocation, often referred to as a “brick and mortar” store. This hasafforded access to salespeople that can share wisdom and provide adviceabout the various products and purchasing options. This is one of theadded benefits of on-site shopping. Unfortunately, the advice issometimes inaccurate, subjective, or a complete fabrication provided todrive sales. The consumer may, or may not, benefit from an onsitepurchase that affords access to product expertise.

Increasingly, consumers are making purchases on-line. On-line shoppingoffers convenience, is expedient, and provides for discreet purchasing.Typically, on-line outlets (i.e., websites) offer consumer productreviews to compete with the on-site expertise of a brick-and-mortaroutlet. Unfortunately, the reviews may be fake, subjective, or justinapplicable.

Consider, for example, the purchase of regulated products such asalcohol, tobacco or cannabis products. When considering a potentialpurchase, the customer may wish to account for certain things such astaste, potency, potential biological effects or other such aspect.Often, such aspects may be governed, at least in part, by the physiologyof the customer (i.e., also referred to as the “user”). Unfortunately,present purchasing outlets, whether on-site or on-line, do little toaccommodate these purchasing decisions and rely heavily on anecdotalopinion or subjective data.

Thus, what are needed are methods and apparatus to accommodate thedecision making process for purchase of regulated products. Preferably,the techniques account for collection and/or use of objective data inthe purchasing decision.

SUMMARY OF THE INVENTION

In one embodiment, a categorization system for objectively categorizinga regulated product for consumption is disclosed, as well as arecommended decision tree which users can utilize in order to navigateproducts and product categories. For ease of reference, “products” and“product categories” as referred to throughout shall refer to cannabisproducts and groups of products, respectively, which are hierarchicallygrouped based on objective data, though the present disclosure isenvisioned for use in connection with products (e.g., alcohol, tobacco,etc.). Each product is either a “strain” of cannabis, or else a productsuch as concentrated extract derived from one or more cannabis strains.The system includes a user interface for displaying individual productsusing a novel form of visualization preferably generated based on labdata quantifying the chemical profile of each product, as well as forreceiving and assessing user-specific data relating to such products.

This product classification schema based on objective data can also beused in combination with user feedback about the subjective effects ofproducts within a category in order to power a recommendation systemwhereby users can be recommended products that will have similar ordifferent effects based on whether they fall into the same or differentproduct groupings, respectively.

In another embodiment, a computer program product comprising machineexecutable instructions stored on non-transitory machine readable mediais provided. The instructions provide for recommending a regulatedproduct for consumption by implementing a method of obtaining user inputincluding at least one of personal data (e.g. sex, age, heartrate, bloodpressure), preference data (e.g. saved history of “liking” or “notliking” a given product), and experience data (e.g. saved history of howa product made them feel); obtaining merchant input including at leastone of general data descriptive of the regulated product, batch datadescriptive of the regulated product and user data descriptive of felteffects of the regulated product on the user; and, making arecommendation of regulated product using a decision engine, therecommendation made according to correlation of the chemical dataobtained from testing laboratories (which is used to categorize andvisualize individual products) with the preference data obtained fromuser inputs.

BRIEF DESCRIPTION OF THE DRAWINGS

The features and advantages of the invention disclosed herein areapparent from the following description taken in conjunction with theaccompanying drawings in which:

FIG. 1 is an schematic diagram depicting a recommendation system;

FIG. 2 is a graphic depicting distributions of products according tocontent;

FIG. 3 is a flow chart providing an exemplary process for generatingrecommendations using the recommendation system disclosed herein;

FIG. 4 is a schematic diagram providing an overview of a system forrecommending products;

FIG. 5 is a sample decision tree represented by the present invention;

FIG. 6 is an example visualization logic utilized by the presentinvention;

FIG. 7 is a further detailed visualization logic as seen in FIG. 6;

FIG. 8 is an alternate further detailed visualization logic as seen inFIG. 6;

FIG. 9 is a further detailed visualization logic as seen in FIG. 7; and

FIG. 10 is sample user-specific output of the recommendation system ofthe present invention.

DETAILED DESCRIPTION OF THE INVENTION

Disclosed herein are methods and apparatus for categorizing andrecommending a product for purchase from a selection of products. Forpurposes of simplicity, the remainder of the disclosure sets forth theembodiment regarding cannabis, though the disclosure is in no waylimiting and any other product—regulated or unregulated—could form thebasis for the categorization and recommendation of the products usingthe methods disclosed herein. However, this is not limiting and merelyillustrative of the technology disclosed herein. There is no requirementthat the products be subject to any particular regulation, unless suchrequirement is expressly stated herein.

The product visualizations and hierarchy of product groups describedherein are preferably determined by objective data comprised ofmeasurements of two major classes of chemical constituents found incannabis: cannabinoids and terpenes. Products are initially classifiedinto one of three major groups defined by the ratio of the two principalcannabinoids found in cannabis products, THC and CBD. The ratio of TotalTHC to Total CBD allows each product to be discretely placed into one ofthree groups in the highest level of the classification hierarchy:“THC-dominant,” “CBD-dominant,” or “Balanced THC/CBD.” Products assignedto one of these three groups can be further grouped into separate groupsnested within these primary groups if they have sufficiently high levelsof another, less common cannabinoid. For example, a “THC-dominant”product may be further distinguished as “THC-dominant +THCV,” indicatingthat it also contains relatively high levels of another cannabinoid.After this phase of classification based on cannabinoid, products arefurther grouped based on measurements of the second class of chemicalcompounds known as terpenes.

The second phase of the classification hierarchy assigns a product to afurther group nested within the larger group assignment defined above.This group assignment is preferably determined by the “dominant” terpenefound in the product, i.e. the terpene present at the highestconcentration for that product type. Cannabis products within each ofthese dominant terpene subgroups are grouped further, at the next levelof the hierarchy, based on the number and rank-order of terpenecompounds present in that product. For example, two cannabis producttypes nested within a single dominant terpene group, which is itselfnested within a higher-level cannabinoid-based grouping, may bedistinguished by having a different number or rank-order of secondary,tertiary, or quaternary terpenes.

These hierarchically-defined cannabis product groupings are preferablydetermined at the highest level based on the major constituents commonlyfound in cannabis known to cause or modulate the psychoactive effects ofthese products (principal cannabinoids such as THC and CBD) and at thelower levels by compounds thought or known to directly impact the flavorand smell, as well as potentially modulate the psychoactive effects, ofcannabis products (terpenes). Thus, products found within the samesub-groups will tend to have similar aromas and likely similar effectsto other products within the same group. Products in differentsub-groups will tend to have different flavor and aroma profiles anddifferent subjective effects (all other things being equal), especiallywhen products fall into different high-level groups. Other hierarchicalcategorizations are also envisioned.

A novel visual design logic is also used to visualize individualproducts (e.g. individual cannabis strains). The visualization for eachstrain is based on objective lab data consisting of measurements of thecannabinoid and terpene profile of that strain or product. “Strains”here refers to common cannabis industry product labels (e.g. “BlueDream”) given to cannabis products. Multiple product lines made bydistinct producer-processors can be given the same strain name label.Lab data comprised of chemical profiles is obtained across multipleproduct lines with the same strain name label, and data are aggregatedand cleaned, and a composite chemical profile is constructed for thatstrain. In addition, the same visualization can be applied to individualproducer-processors' products, allowing similarities or differences inspecific product lines to be visually discerned or compared to thecomposite visualization of all products with the same strain name.

As seen in FIG. 5, strains of cannabis product can be classified andsub-classified in the same manner as wine, for example. Just as a winecan be categorized into white wine or red wine, and then further intothe type of wine and the vintage, cannabis product can be categorized atmany levels by its components, its effects, and its “vintage”.

As seen in FIG. 6, the preferred visualization logic used to createvisual representations for individual products (a specificproducer-processors product) or “strains” (composed of data aggregatedacross product lines sharing the same lab) is as follows: first, thehighest level grouping for a cannabis product (e.g., THC-dominant,CBD-dominant, or Balanced) is provided, and can be represented bycentral shape 500. As seen in FIG. 5, this shape is located at thecenter of the visualization, and in the example provided, the highestlevel grouping of the cannabis product is identified as THC-dominant.Next, additional rings (e.g., R1, R2, R3, R4, etc.) can be added to thecentral shape 500, with the number of additional rings preferablydetermined by the total number of “significant” terpenes for thatproduct. Once the number of significant terpenes are identified andadditional rings are added to the visualization logic, additional shapes(e.g., S1, T1, etc.) can be populated along these rings, as seen inFIGS. 7-9. The shapes S1, T1 themselves can represent different types offeatures of the highest level grouping of the product seen in thecentral shape 500. For example, rectangular shapes with sharp edges androunded shapes can represent THC and CBD content of the product,respectively. The colors of the shapes can be dictated by major terpenespresent in the product.

The example visualization logic seen in FIGS. 7 and 8 are complementary,with FIG. 7 highlighting rings R1 and R4, while FIG. 8 highlights ringsR2 and R3 of the same visualization logic. In the visualization logic,rings R1 and R4 preferably both encode information about the dominantterpene, which also dictates the dominant+background color of eachvisual. Rings R2 and R3 preferably encode information about secondaryand tertiary terpenes, where applicable. Therefore, like the examplevisualization logic set forth in FIGS. 7 and 8, all visualization logicsfor product characterizations and recommendations preferably compriserings designating dominant terpenes, with optional additional ringsrepresenting additional secondary and/or tertiary (etc.) terpenes.

Further, in the visualization logic seen in the Figures, shape size andcolor are related to the levels of a given terpene. More specifically,the length and width of the shapes surrounding the central shape arepreferably determined by THC and CBD levels. In the examplevisualization logic set forth in FIGS. 5-9, higher THC or CBD levelsresult is more elongated rectangular or oval shapes, respectively. Forexample, a product with very high THC levels will have long, thinrectangular shapes that appear “pointy,” whereas a product with low THClevels will contain rectangular shapes that appear less elongated andmore square. Products containing THC and little or no CBD contain onlyrectangular shapes, products with CBD and little or no THC contain onlyrounded shapes, and products with a mixture of THC and CBD contain bothkinds of shapes. Each shape can be either a full or half shape (e.g.full square vs. a half square). The fullness and total number of shapesis determined the levels of that products major terpenes. Of course,alternative visualization logic sets can be used in accordance with asystem or user preference.

As seen in the example of FIG. 9, the color-coding of each visualizationis based on that products terpene profile, with the primary colordetermined by the “dominant” terpene, i.e. the terpene present and thehighest levels. In this example visualization logic, each of the majorterpenes is represented by a unique color, and each product representedby a visualization logic contains a subset of these colors based on itsparticular composition.

This hierarchical system of organization and visualization, based onobjective lab data measurements of the composition of cannabis products,also serves as the basis of recommendation system. In response to simpleuser inputs (e.g. questions about their desired psychoactive effects),users can be recommended to try products within distinct groupings inthe hierarchy. An example of the manner in which products arerecommended to users based the classification model can be seen in FIG.10.

Generally, the methods and apparatus (also referred to as a“recommendation system”) receive input from a potential purchaser (i.e.,a “user”). The user input includes user data which includes, forexample, user preferences such as taste, level of psychoactive effects,and the like. Other user data may include demographic values, such asage, sex, or experience level consuming cannabis products.

The recommendation system may be configured with a database or librarythat contains product information. The product information isdescriptive of the regulated products in ways that will provide forcorrelation with the user data and improve fulfillment. Generally, thecorrelation of user data and product information is according toalgorithms implemented by the recommendation system, thus enablingselection of products that closely correlate to customer requests.

Prior to discussing the technology disclosed herein in detail, aspectsof some terms are now introduced.

As discussed herein, the term “user account” generally refers to anaccount maintained on behalf of a user to facilitate at least one oftracking of user data, selection and order of a regulated product. Asdiscussed herein, the term “merchant account” generally refers to anaccount maintained on behalf of a merchant to facilitate evaluation ofmerchant operations, such as sales operations, orders placed withsuppliers, inventory and other related information for user selectionand acquisition of the selected regulated product.

As discussed herein, the term “merchant” as well as “online store” and“website” are related. These terms generally refer to offerings byanother (the merchant) accessible through a network, such as throughbrowser over the Internet. As discussed herein, the merchant offersgoods and/or services for sale to shoppers (which are also referred toherein as “users” of the user application).

Referring now to FIG. 1, an exemplary embodiment of a recommendationsystem 100 is shown. In this example, the recommendation system 100permits a user making use of a user device 110 to securely, rapidly andautomatically complete a purchase transaction. In this example, eachuser device 110 shares a common user account 105. The user account 105provides for convenient storing and sharing of information between userdevices 110.

Exemplary user devices 110 as may be used in the recommendation system100 include, without limitation, a personal computer (PC) 111, a laptop112, a tablet computer 113, a smartphone 114, and a biometric monitor115. Generally, each user device 110 includes a display 120. Each of thedisplay 120 offer the user a visual interface for interaction with therecommendation system 100. For example, the recommendation system 100may be presented as a browser interface that makes use of knowntechniques for user interaction.

Generally, each user device 110 is in communication with network 150through communications channel 160. The network 150 is also incommunication with merchant server 181 and may further communicate witha supplier server 182. In this example, merchant server 181 containsinstruction sets governing merchant operations and serves a plurality ofuser accounts 105. Supplier server 182 may be an e-commerce systemserver generally configured for transactions between suppliers(wholesalers) and merchants (retailers) and further, the supplier server182 may contain product information beyond that which is supplied to themerchant. The foregoing are merely illustrative of the architecture ofthe recommendation system 100 and is not meant to be limiting.

In this example, any user device 110 may include conventional softwaresuch as productivity tools (e.g., word processing, spreadsheets, etc.)and at least one browser. Tablet computer 113 or smartphone 114 may alsoinclude at least one “app” (defined generally as a purpose orientedapplication that may include network communications as part of thefunctionality), as well as a biometric sensor 124 that can be aconventional optical scanner configured with an appropriate app for useas a fingerprint reader. The fingerprint reader may include software forreceiving data from the scanner and interpreting the data within thecontext of a fingerprint. Other user devices 110 may include a biometricsensor 124 and/or other equipment useful for implementing authenticationschemes. Thus, the recommendation system may further implement securitymeasures for securing access to the user device 110.

The biometric device 115 may be a personal fitness device or anotherspecialized device. Generally, the biometric device 115 collects andprovides personal data. The personal data may be provided to another oneof the user devices 110 or directly to the merchant system 181. Personaldata that may be collected by the biometric device 115 includes, forexample, heart rate, body temperature, blood pressure and other suchparameters. The personal data may be communicated to other components ofthe recommendation system 100.

Once a user new to the recommendation system 100 has established theuser account 105, the user may then enter the user data including atleast one of user preferences, financial information and user physiologydata for storage in the user account 105. Data entry into the useraccount 105 may be performed manually and/or electronically. Electronicdata entry may include, for example, electronic entry of baselinepersonal data for use as a control or for comparison sake to collecteduser input data. The baseline personal data for the user may includephysiological parameters collected in a normal, resting state for theuser.

Once the user has established the user account 105, the user may loginto the product categorization and visualization system, which therecommendation system is built upon, and at any time search forofferings of a merchant for a specific regulated product, or may viewoffers of regulated products from respective merchants. The user mayenter preference data or experience data to select a specific productand determine a merchant offering the same. Alternatively, where theuser is unfamiliar with the regulated products or may not have anypreference data or experience data for input, the recommendation systemmay make recommendations based on the user data.

For example, in the case of cannabis, the user may be presented with aseries of questions by the recommendation system that support a decisiontree. Examples of questions for the user include:

Question Answer Do you want to avoid getting high? Y/N Do you want toexperience psychoactive Y/N effects but arenervous/sensitive/lightweight Do you definitely want to get high? Y/N Doyou want psychoactive effects but are Y/N worried about certainside-effects, e.g. anxiety or hunger. Have you tried a specific productbefore? Y/N Are you interested in a product for pure Y/N recreationalpurposes or also for medicinal use? Do you have a preference for aparticular form Y/N of product, such as natural form, cooking additive,oil, capsule/pill form, candy or beverage? If so, check any applicableform from those listed below. Natural form € Cooking additive € Capsuleor pill € Candy € Beverage € Prepared foods € Other €

At this level of decision tree, the recommendation system begins to biasselection of cannabis products based on content of the variouspsychoactive substances either at relatively high levels of theorganization hierarchy (i.e., strains that are THC-dominant,CBD-dominant, or Balanced THC/CBD) or at lower levels (i.e. terpenegroupings) and the effects that they provide to users. Reference may behad to FIG. 2 which depicts distributions of cannabis products accordingto content.

At the next level down in the decision tree, the user may be askedadditional questions that further sub-categorize products within ageneral product category, such as whether there are specific flavorsthey prefer, whether they want strains that are better for certainissues, and others. Both levels and the batch-specific level could befurther personalized with more specific recommendations with relevantuser feedback, either based on population-level data (if user is brandnew) or at the individual level (if a user has provided us with enoughfeedback historically).

In FIG. 2, the shaded areas depict a population of data points, witheach data point representing total content of CBD and THC for aparticular strain of cannabis. Darker areas indicate a greaterprevalence of strains.

The CBD-dominant strains may be preferred by medical patients or highlysensitive consumers who want to avoid getting high. THC-dominant strainsmay be preferred by experienced consumers and those seeking a “classic”cannabis experience. Balanced strains may be preferred by novice orsensitive consumers who want a milder high with fewer THC-induced sideeffects.

The selection process may continue with questions such as: are therespecific flavors you prefer? Do you want strains that may be better orworse for [certain things], which could include anxiety, inflammation orpain, relaxation, sleepiness, or if the user has a cost limit?

The recommendation system 100 may be further personalized with morespecific recommendations with relevant user feedback, either based onpopulation-level data (if user is brand new) or at the individual level(if a user has provided prior historical feedback).

Once the selection process has received the requested preferenceinformation, the recommendation system 100 will make recommendations tothe user. The recommendations may include identity of a particularstrain of cannabis, a recommended quantity for ingestion, recommendedtechniques for ingestion, and other such aspects.

The recommendations may be based upon a variety of factors. For example,the recommendation system 100 may provide each user with a specific userexperience feedback facility. The user experience feedback facility mayquery the user for a variety of parameters. Questions may solicitinformation regarding, for example, a degree of euphoria, hunger,queasiness, relaxation and other such subjective aspects. Other input tothe user experience feedback facility may include objective data such aspersonal data collected by the biometric device 115.

The user experience feedback may result in refined personalrecommendations for future purchasing. In some embodiments, userexperience feedback is aggregated. Aggregated user experience feedbackmay be used to develop and refine a heuristic algorithm for makingrecommendations to new users or users with changed input data.

FIG. 3 is a process diagram depicting one aspect of user input andongoing updating of the recommendation system. In FIG. 3, the userinitiates a request for a recommendation. User data 310, such aspreference data 312 and/or personal data 311, are input into therecommendation system 100. When considering the inputted user data andaspect of CBD and THC content related to the products which fall withinthe classification of the user's preference data 312, the recommendationsystem 100 makes reference to the appropriate data set, such as that setforth in FIG. 2, to identify the target on the grid as to a product orproducts falling within recommended THC-CBD content percentages. Fromthe user data 310 and the content information (FIG. 2), therecommendation system 100 narrows the pool of products 200 forrecommendation to a limited set. In this illustration, the candidateproducts 200 for recommendation are denoted as A, B, C, D and E. Ofcourse, the CBD and THC content are merely one aspect or parameterconsidered by the recommendation system 100. Consideration of thevarious other salient parameters may result in recommendation of fewercandidate products 200. Once selected, the user 250 will indulge andhave an experience 313. The experience data is tracked and used toassist with future recommendations.

If, however, a user does not agree with the recommendations of products200 provided by the recommendation system 100, the user can providefeedback of experience data 313 or additional preference data 312 toalter and update the recommendation. For example, the user may indicatethat the recommended products 200 contain either too much or too littleTHC, too much or too little CBD, will cause an effect that differs fromthe user's preference, etc. Based on this feedback, the recommendationsystem 100 can update the user data 310 and re-assess the totality ofthe inputted information to update, in real time, a revised set ofrecommended products 200. This is process is set forth with furtherdetail in FIG. 4.

FIG. 4 is a block diagram depicting aspects of the recommendation systemand is useful for describing an exemplary embodiment of a process formaking a recommendation of regulated product.

As shown in FIG. 4, user data 310 and product data 320 are provided asinputs to a decision engine 330. In this example, the user data 310includes baseline personal data 311, preference data 312, and experiencedata 313. Generally, the baseline personal data 311 includes objectiveaspects such as, without limitation, age, sex, race, weight, heart rate,blood pressure, and the like. The user preference data 312 may includeinformation related to the type of experience sought and the subjectiveview of the user as it relates to specific products or product effects.The preference data 312 may be entered contemporaneously, and may bestored with default values suited for the particular user. Generally,the experience data 313, which may be collected during the userexperience, is descriptive of the user experience and therefore may beused to develop a degree of conformity to the product description. Theexperience data 313 may include a subjective component collected fromuser assessments and reviews, and may also include an objectivecomponent, such as updated personal data 311 of the user when under theinfluence. Examples of personal data that may be monitored while underthe influence include aspects such as heart rate, blood pressure,thirst, food intake, and the like.

Generally, the product data 320 includes general data 321, batch data322, and clinical data 325. The general data 321 generally includesaspects such as name, supplier identity, and fundamental aspects such asaverage content, density, storage recommendations, manner ofconsumption, and the like. The batch data 322 may include morespecifics, including actual test results from a laboratory or scientificresearch on a given product. The clinical data 325 may include objectivedata, such as that derived from administration in a controlled settingwhere physiological parameters are monitored, and may include subjectivedata, such as user feedback. The clinical data 325 may be derived fromthe experience data 313 of multiple users, taking into accountvariations between batches, user physiology, and the like. Statedanother way, the clinical data 325 may be experience data 313 that isnormalized over a statistically significant population using aspectssuch as the baseline personal data 311, and therefore predictive of auser experience for a new user.

Generally, the recommendation system 100 will task the decision engine330 with the task of making a recommendation 340 for a particular user.That is, for each user, given a set of respective baseline personal data311, preference data 312, and experience data 313, the decision engine330 may apply a heuristic algorithm that derives recommendations 340from other sets of baseline personal data 311 maintained in therecommendation system 100. Generally, the recommendations 340 arearrived at by using large data sets to improve correlation between theexpressed preferences 312 and the experience 313, combined and comparedwith general data 321, batch data 322, and clinical data 325 regardingeach potential product for recommendation.

The recommendation system 100 may also include a translation engine 350.Generally, the translation engine 350 weights user experience data 313according to the personal data 311 to provide for additions to theclinical data 325. In some embodiments, translation (or correlation) isperformed by the decision engine 330 during processing.

As one may surmise, the iterative processing of large data sets withdiverse data lends itself well to use of artificial intelligence.Accordingly, the decision engine 330 may implement artificialintelligence. The artificial intelligence may be provided as a neuralnetwork, for example. In one embodiment, the neural network makes use ofthe user preferences 312 as the input layer, and applies aspects such asthe baseline personal data 311 and batch data 322 in the hidden layers,and then may continuously update the information stored and reviewed forpossible recommendation 340 based on changes to the user preference data312 or user experience data 313, or updated information to productclinical data 325. The artificial intelligence may also continue tounderstand the baseline personal data 311 for desired effect of theregulated product, and understand how it compares to the objectiveclinical data 325 of numerous users, with the both the user data 310 andthe product data 320 able to update in real time.

The recommendation system 100 may also aid in the procurement process.That is, for at least one product recommendation, the recommendationsystem 100 may then query the respective merchant servers 181 (and/orsupplier servers 182) to identify availability of the recommendedproduct and location for pick-up of the same, which recommendation maybe made by assessing a geolocation of a user. Once sources (i.e.,merchants) for the recommended product have been identified, anyregulatory constraints on a transaction can be identified and may beused to qualify availability and recommendations. For example, if aprescription or medical use license is required by a state where pick-upof inventory for the recommended product may be available, then the usermay be alerted to the requirement. Alternatively, the recommendationsystem 100 may qualify users and conceal the availability of therecommended product or particular locations for pick-up from users thatare disqualified from purchase on the basis of a respective userprofile, as discussed below.

Limitations on procurement may be specific to each of the products, andtherefore may be tracked in the recommendation system 100 as a part ofthe product data 320. Some limitations on procurement may be specific toexternal factors, such as jurisdictions laws or regulations. Forexample, one jurisdiction may require early closing of dispensarieswhile a neighboring jurisdiction permits extended hours of operation. Avariety of potentially regulated parameters may be tracked by therecommendation system 100, all of which may factor into the recommendedproducts 200. Examples of external factors that may govern transactionsunder applicable laws or regulations include, without limitation: statelaws, hours of operation, merchant licensing, sales limitations, userage, prescription requirements, use licensing, residential information,user restrictions, user prohibitions, criminal records, credit, andother such factors.

Accordingly, the recommendation system 100 may include a regulatorydatabase. The regulatory database may be in communication with otherregulatory tracking services, such as LEXIS or WESTLAW. As theregulatory database may be accessed during the process of recommendationgeneration and maintained up-to-date, the recommendation system 100 mayaid merchants and users with regulatory compliance, which may alsoaffect the recommendations made by recommendation system 100 to fulfillor comply with applicable laws or regulations in the regulatorydatabase. This will ensure that any recommendation for a regulatedproduct made by the recommendation system 100 is ideally in accordancewith applicable laws.

Taken together, the recommendation system 100 can identify one or morerecommended products 200 based on input of any one or more of objectivelab testing data, subjective user preference data, product data 320,geolocation of a user, and regulatory, legal, or other limitationsregarding locations at which recommended products 200 may be provided.Therefore, the recommended products 200 will be ones which (1) the usercan conveniently and legally pick up, (2) are available for purchasewith sufficient inventory at identified merchants 181, (3) match theuser's preferences for manner of consumption, graphical indication ofTHC-CBD content (as seen in FIG. 2), (4) match the user's subjectivepreferred experience, and (5) fit the user's objective personal data toconfirm appropriate and advisable levels of recommended consumption.

Having introduced aspects of the recommendation system for makingrecommendations, selections and purchasing of regulated products, someadditional features and aspects are now introduced.

Advantageously, the recommendation system disclosed herein provides forrecommendations that correlate well with user preferences. That is, forexample, by implementation of a public system, a large set of user dataand product data is attainable. This large data set, in combination withsophisticated algorithms such as a neural network, provides forrecommendations with a degree of sophistication that is not attainableby a salesperson, medical practitioner, or other such individual.Further, such recommendations may be made with complete privacy, at anytime of day. By employing the recommendation system with a regulationdatabase, regulatory compliance may be assured, thus removing theresponsibility for compliance assurance from sales staff, thus enhancingsales.

The recommendation system may be provided as a set of machine executableinstructions on non-transitory machine readable media. Generally, eachuser device is configured to store machine executable instructions onnon-transitory machine readable media (such as in read-only memory(ROM), random-access-memory (RAM), or in a non-volatile storage unitsuch as a hard disk, solid state drive, or the equivalent). The machineexecutable instructions may be referred to herein as “software,” as an“application,” as a “client,” a “process,” a “plug-in,” an “add-in,” an“add-on,” an “extension,” and by other similar terms. The machineexecutable instructions generally provide for functionality throughoperation of various methods as may be presented herein as well asothers that may be apparent to those skilled in the art.

Some of the machine executable instructions stored on non-transitorymachine readable media may include an operating environment alsoreferred to as an operating system. For example, and as presentedherein, a suitable operating environment is WINDOWS (available fromMicrosoft Corporation of Redmond Wash.). Other operating environmentsinclude iOS from Apple of Cupertino Calif. and ANDROID available fromAlphabet of Mountain View Calif. Software as provided herein may bedeveloped in, for example, SQL language, which is a cross-vendor querylanguage for managing relational databases. Aspects of the software maybe implemented with other software. For example, user interfaces may beprovided in XML, HTML, a variety of scripting languages and the like.

More specifically, given the highly configurable nature of computingsystems, the terms “computer” and “user device” as well as other similarterms are to be construed to include any configuration of componentsand/or software as needed to provide for the intended functions as wellas extensions thereof. The architecture of the recommendation system maybe modified as deemed appropriate for implementation.

The recommendation system may be implemented over a network, such as theInternet.

The biometric device may be a commercially available device, such as aFITBIT personal electronic device, an APPLE WATCH personal electronicdevice, or any other similar type of device. The biometric device may bea specialized device configured for operation with the recommendationsystem. The biometric device may include additional functionality. Forexample, the biometric device may provide user alerts, a screen or otheruser interface to facilitate user interaction with the recommendationsystem or other such functionality.

The recommendation system may implement self-training algorithms such asartificial intelligence. For example, the recommendation system mayimplement a neural network. The neural network may accept, for example,personal data to correlate observed physiological effects of productswith the physiology of the user. The correlation may be for theindividual user, a segment of the user population, or the userpopulation as a whole.

Supplier data, which may be useful for formulating recommendations, mayinclude without limitation, density, chemical composition, such as fromlaboratory analyses of regulated products, vintage, source,certification by an appropriate standard, a set of user experiencefeedback, reviews and any other data deemed appropriate. The supplierdata may be input through a standardized interface for communicationbetween suppliers and merchants.

The recommendation system may implement user security, privileges,certificates, and other techniques to ensure integrity of the processand authenticity of the regulated products delivered to the user. Therecommendation system may be operated with regard for privacy laws, suchas HIPAA (i.e., the “Health Insurance Portability and AccountabilityAct”) which sets forth requirements for control of medically sensitiveinformation.

The recommendation system may include advertising. The recommendationsystem may be used to sell other items such as related paraphernalia.The recommendation system may be configured for facilitating routine orautomatic ordering.

Various other components may be included and called upon for providingfor aspects of the teachings herein. For example, additional materials,combinations of materials and/or omission of materials may be used toprovide for added embodiments that are within the scope of the teachingsherein.

Any readers of any patent issued on this application should note thatApplicants do not intend any of the appended claims or claim elements toinvoke means-plus-function terminology as related to 35 U.S.C. section112(f) unless the words “means for” or “step for” are explicitly used inthe particular claim.

When introducing elements of the present invention or the embodiment(s)thereof, the articles “a,” “an,” and “the” are intended to mean thatthere are one or more of the elements. Similarly, the adjective“another,” when used to introduce an element, is intended to mean one ormore elements. The terms “including” and “having” are intended to beinclusive such that there may be additional elements other than thelisted elements.

While the invention has been described with reference to exemplaryembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications will be appreciated by those skilled in theart to adapt a particular instrument, situation or material to theteachings of the invention without departing from the essential scopethereof. Therefore, it is intended that the invention not be limited tothe particular embodiment disclosed as the best mode contemplated forcarrying out this invention, but that the invention will include allembodiments falling within the scope of the appended claims.

What is claimed is:
 1. A recommendation system for recommending aregulated product for consumption, the system comprising: a userinterface for receiving user input from one or more user devices, theuser input including personal data, preference data, and experiencedata, wherein the experience data includes health-monitor data from awearable user device, wherein the health monitor data representsreal-time physiological condition of a user; a merchant interface forreceiving merchant input, the merchant input including general datadescriptive of the regulated product, batch data descriptive of theregulated product and clinical data descriptive of physiological effectsof the regulated product on a set of users; and a decision engineconfigured to dynamically update, via a neural network, the clinicaldata based on normalizing the health-monitor data using baseline dataand over an identified population having commonalities in the personaldata, wherein the identified population is a subset within the set ofusers, the baseline data for the user includes physiological parameterscollected in a normal resting state of the user, and the clinical dataincludes other health-monitor data normalized based on other baselinedata to account for variations in physiologies across the set of users,the other health-monitor data and the other baseline data both collectedfrom the set of users, predict a user experience for the user based on acorrelation between the health-monitor data of the user and the otherhealth-monitor data of the identified population, and make arecommendation of the regulated product, the recommendation selectedaccording to a correlation between the user input and the merchantinput, the dynamically updated clinical data, and the predicted userexperience.
 2. The recommendation system as in claim 1, wherein thepersonal data comprises at least one of age, sex, desired level ofpsychoactive effects, or experience level with cannabis products.
 3. Therecommendation system as in claim 1, wherein the preference datacomprises a desired physiological effect from consumption of theregulated product.
 4. The recommendation system as in claim 1, whereinthe experience data comprises at least one of objective data andsubjective data.
 5. The recommendation system as in claim 4, wherein theobjective data includes the health monitor data collected with thewearable user device.
 6. The recommendation system as in claim 1,wherein the regulated product is cannabis.
 7. The recommendation systemas in claim 1, wherein the decision engine is configured to develop adegree of conformity between the health-monitor data collected during auser experience of the regulated product and the general datadescriptive of the regulated product, wherein the degree of conformityis used to make the recommendation of the regulated product.
 8. Therecommendation system as in claim 1, further comprising: a translationengine configured to weigh the experience data according to the personaldata for making the recommendation.
 9. The recommendation system as inclaim 1, further comprising: a visual design logic configured togenerate a visual label representative of an individual product, whereinthe visual label uses a shape, a color, a location of the shape and/orcolor, an arrangement thereof, or a combination thereof to communicatecharacteristic traits of the individual product to the user.
 10. Therecommendation system as in claim 9, wherein the visual label represents(1) a primary chemical categorization associated with psychoactiveeffects and (2) one or more secondary components associated with detailsof the primary chemical categorization and/or additional chemicalcomponents associated with secondary experiences provided to the user bythe individual product.
 11. A computer program product comprisingmachine executable instructions stored on non-transitory machinereadable media, the instructions for recommending a regulated productfor consumption by implementing a method of: obtaining user input fromone or more user devices, the user input including demographic data,preference data and health-monitor data from a wearable user device,wherein the health-monitor data represents real-time physiologicalcondition of a user; obtaining merchant input including general datadescriptive of the regulated product, batch data descriptive of theregulated product and objective data descriptive of one or more effectsof the regulated product on a set of users; dynamically updating, via aneural network, the objective data based on normalizing thehealth-monitor data using baseline data and over an identifiedpopulation having commonalities in the demographic data and/or thepreference data, wherein the identified population is a subset withinthe set of users, the baseline data for the user includes physiologicalparameters collected in a normal resting state of the user, and theobjective data includes other health-monitor data normalized based onother baseline data to account for variations in physiologies across theset of users, the other health-monitor data and the other baseline databoth collected from the set of users; predicting a user experience forthe user based on a correlation between the health-monitor data of theuser and the other health-monitor data of the identified population, andmaking a recommendation of the regulated product using a decisionengine, the recommendation selected according to a correlation betweenthe user input and the merchant input, the dynamically updated objectivedata, and the predicted user experience.
 12. The computer programproduct as in claim 11, further comprising a regulations database forlimiting the recommendation according to prevailing law.
 13. Arecommendation system for recommending a regulated product forconsumption, the system comprising: a user interface for receiving userinput from one or more users and user health data from a user deviceconfigured to provide one or more physiological measurements of a user;a merchant interface for receiving merchant input, the merchant inputincluding product description data descriptive of the regulated product,and clinical data descriptive of physiological effects of the regulatedproduct on a set of users; and a decision engine configured todynamically update, via a neural network, the clinical data based onnormalizing the user health data using baseline data and over anidentified population having commonalities with the user input, whereinthe identified population is a subset within the set of users, thebaseline data for the user includes one or more baseline physiologicalmeasurements collected in a normal resting state of the user, and theclinical data includes other health-monitor data normalized based onother baseline data to account for variations in physiologies across theset of users, the other health-monitor data and the other baseline databoth collected from the set of users, predict a user experience for theuser based on a correlation between the user health data of the user andthe other health-monitor data of the identified population, and make arecommendation of the regulated product, the recommendation selectedaccording to a correlation between the user input, the user health data,the merchant input, the dynamically updated clinical data, and thepredicted user experience.